DATA4600 Business Analytics Project Management Case Study 1 Sample

DATA4600 Business Analytics Project Management

Assessment Instructions

Part 1 – Case Study

Business case study: How to Adapt Gas Stations for Electric Vehicles

With the popularity of electric vehicles on the rise, there will be more demand for charging stations throughout Australia, especially when drivers are making longer journeys cross country.

Gas stations are uniquely positioned to adapt to these changes and incorporate charging stations into their infrastructure. The article below discusses some of the issues and opportunities for gas stations.
https://www.forbes.com/sites/forbesbusinesscouncil/2023/08/24/how-to-adapt-gas-stations-for- electric-vehicles/?sh=704fdd4052ec

Imagine you have been asked to lead the following project. A small team of analytics specialists have been asked to collaborate with the gas station infrastructure team to place new electric charging vehicle stations in locations throughout Australia – both metro and rural areas.

The main aim of the project is to perform analytics to identify the best locations for the charging stations and provide suitable on-going analytics to track and monitor the success and usage of the new charging station infrastructure.

This assessment is to be completed individually. The written assessment will be completed form of a report in Microsoft word submitted via Turnitin. The presentation will be a video uploaded to the KBS system

PART 1A – Written Paper

With the CRISP-DM overall methodology in mind, answer the following questions and produce a written report.

Section 1 – Project Set-Up

• Introduce the project and define 3-4 key business problems to be solved in relation to this project.

• Set 2-3 relevant KPI’s for the project. These should be related to the business questions, and may be concerned with timelines, data collection or analytics methods.

• Describe the specific data required for the project and how it will be used.

Section 2 – Project Methodology

• Describe the features of the waterfall project methodology and how it would be specifically applied to this project.

• Describe the features of one of the newer Agile/Hybrid project methodologies (Scrum, Kanban,etc.) and how it could be specifically applied to this project.

• Suggest why some of the methodologies may fail in this specific case and which method you would recommend as the best fit for project success.

• Describe possible issues you may encounter collecting data and using analytics in this specific project.

Section 3 – Structure, Context and Referencing (2 marks)

• Find at least five supporting references for your report. List the references in Harvard format. Answers should be in the context of CRISP-DM. Structure your work as a report.

Solution

Part 1A

Project Set-Up

Project Introduction and 4 Key Business Problems

The project is focused on adaption of Gas station for electric vehicles and way in which the gas station adoption project can effectively be implemented. The increasing popularity of electric vehicles (EVs) is driving a surge in demand for charging stations across Australia. The project also focuses on the creation of an ongoing analytics platform to track station performance and utilisation, ensuring a data-driven approach to continual improvement (Katz, 2023).

i. Assessing Station Capacity and usage Requirements- To ensure effective operations and minimise congestion, the optimum number of charging stations per site must be evaluated based on consumption trends.

ii. Usage Monitoring and Optimization- Real-time monitoring, machine learning techniques for forecasting high use periods, and continual optimisation to improve station availability and user experience are all part of this (Katz, 2023).

iii. Identifying Optimal Locations- Selecting strategic sites for EV charging stations that correspond to EV travel patterns and demand is critical for maximising utilisation and profitability for MBA assignment expert.

iv. Integration of Infrastructure- Successfully integrating charging stations into various petrol station layouts creates technological obstacles. It is critical for project success to ensure an effortless and cost-effective integration while conforming to regulatory criteria (Katz, 2023).

3 Relevant KPIs for the Project

i. Percentage of EV charging stations installed on time and within budget

ii. Customer satisfaction with EV charging experience

iii. Real-time Analytics Accuracy and Average charging station utilization rate

Data Required for the Project and its Use

Table 1: Data Required for the Project and its Use

Section 2 – Project Methodology

Features Of The Waterfall Project Methodology And Its Application In The Project

With the help of CRISP- DM framework, the identified key features of waterfall project methodology and its application in Gas station project are as mentioned below.

Business Understanding

● The objectives of this project are to find the ideal locations to put electric vehicle charging stations and to provide continuous analytics to track how well they work and how much power they use.

● Learn to know the business landscape, which includes things like the increasing need for electric vehicle charging stations, petrol stations' advantageous position to adapt, and the possible financial gains (Al Gharbi et al., 2021).

Data Understanding

● Locate and collect the right data, including EV use trends, population statistics, information on petrol station infrastructure, and financial and economic statistics.

● Analyse the numbers to find out where charging stations can be located, how often people use electric vehicles, and what regions people live in (Salz and Hotz, 2021).

Data Preparation

● Cleaning, transforming, and integrating data from many sources are all part of the preprocessing process.

● Build a central database that can be queried and analysed with ease.

Modelling

● Create models that can foretell where electric vehicle charging stations will be most needed.

● Consider about analysing the data and making predictions using machine learning techniques like decision trees or regression.

Evaluation

● Validate the models with other sources of data or simulation methods to make sure they're accurate.

● Improve the models' accuracy and prediction capacity by refining them depending on the assessment outcomes (Montoya et al., 2023).

Deployment

● Incorporate the prediction models into a decision-support platform that petrol station managers may use.

● Create an all-encompassing system for tracking station use, performance, and financial feasibility via review and monitoring (Firas et al., 2023).

Figure 1: Waterfall Methodology
(Source: Singh, 2022)

Features Of One Of The Newer Methodology And Application In This Project

The selected Agile project methodology is Scrum. Scrum is an incremental and iterative Agile methodology that uses two- to four-week long "sprints" to build new features. Each sprint should culminate in the delivery of a potentially shippable product increment, with an emphasis on collaboration and flexibility. Key features include the Product Backlog (a prioritized list of features), Sprint Planning, Daily Stand-ups, and Sprint Review (Milicevic et al., 2019).

Application to the EV Charging Station Project

● Sprint Planning (Location Analytics)- analysing GIS data to find charging station locations may be the focus of one sprint. Maintaining a consistent rate of improvement and flexibility requires regular planning meetings (Hayat et al., 2019).

● Daily Stand-ups (Infrastructure Integration)- The analytics and infrastructure teams are able to coordinate their work, solve problems, and guarantee a seamless integration via brief daily meetings called stand-ups.

● Sprint Review (Analytics Framework Development)- Each sprint concludes with a review and adaptation of the analytics framework, whereby the team takes into account feedback and lessons gained to ensure continual progress (Reddy et al., 2021).

● Backlog Refinement (Ongoing Monitoring and Optimization)- Project requirements can be dynamically fulfilled by continuous monitoring and optimisation of the product backlog, which is updated to prioritise features according to real-time use data and input from stakeholders.

Figure 2: Scrum methodology
(Source: Covetus, 2019)

Reason Behind Failing Of Some Methodologies And Best Recommended Methodology

While organised and well-defined, the Waterfall method could face issues in this project due to its inflexible nature and limited adaptation to changing needs. Complex considerations such as growing EV technology, customer demands, and market circumstances all play a role in the EV charging station project. The sequential approach of the Waterfall technique may not be able to accept these changes effectively (Rush and Connolly, 2020). In this particular scenario, it seems that Scrum is an ideal match for project success. Its adaptability, flexibility, and focus on continual development are ideally suited to the project's objectives and the volatile nature of the EV charging station industry. Scrum will allow the team to adapt to changes efficiently, integrate new insights, and optimise project results throughout its duration.

Possible Issues Encountered While Collecting Data And Using Analytics In This Specific Project

Table 2: Data Collection Issues

Table 3: Analytics Challenges

References

Al Gharbi, S., Al-Majed, A., Abdulraheem, A., Patil, S. and Elkatatny, S., 2021, June. Using data-mining CRISP-DM methodology to predict drilling troubles in real-time. In IADC/SPE Asia Pacific Drilling Technology Conference. OnePetro.

Covetus. (2019). Agile Scrum Methodology for Project Management. [online] Available at: https://www.covetus.com/blog/scrum-methodology-agility-with-endurance [Accessed 24 Nov. 2023].

Firas, O., 2023. A combination of SEMMA & CRISP-DM models for effectively handling big data using formal concept analysis based knowledge discovery: A data mining approach. World Journal of Advanced Engineering Technology and Sciences, 8(1), pp.009-014.

Hariri, R.H., Fredericks, E.M. and Bowers, K.M., 2019. Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), pp.1-16.

Hayat, F., Rehman, A.U., Arif, K.S., Wahab, K. and Abbas, M., 2019, July. The influence of agile methodology (Scrum) on software project management. In 2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 145-149). IEEE.

Katz, E.Y. (2023). Council Post: How To Adapt Gas Stations For Electric Vehicles. Forbes. [online] 25 Aug. Available at: https://www.forbes.com/sites/forbesbusinesscouncil/2023/08/24/how-to-adapt-gas-stations-for-electric-vehicles/?sh=33b557f352ec [Accessed 24 Nov. 2023].

Milićević, J.M., Filipović, F., Jezdović, I., Naumović, T. and Radenković, M., 2019. Scrum agile framework in e-business project management: an approach to teaching scrum. European Project Management Journal, 9(1), pp.52-60.

Montoya-Murillo, D., Mora, M., Galvan-Cruz, S. and Muñoz-Zavala, A., 2023. A Selective Conceptual Review of CRISP-DM and DDSL Development Methodologies for Big Data Analytics Systems. Development Methodologies for Big Data Analytics Systems: Plan-driven, Agile, Hybrid, Lightweight Approaches, pp.123-160.

Reddy, P.C., Nachiyappan, S., Ramakrishna, V., Senthil, R. and Sajid Anwer, M.D., 2021. Hybrid model using scrum methodology for softwar development system. J Nucl Ene Sci Power Generat Techno, 10(9), p.2.

Rush, D.E. and Connolly, A.J., 2020. An agile framework for teaching with scrum in the IT project management classroom. Journal of Information Systems Education.
Saltz, J. and Hotz, N., 2021. Factors that influence the selection of a data science process management methodology: an exploratory study.

Singh, R. (2022). Waterfall Methodology. [online] Institute of Project Management. Available at: https://www.projectmanagement.ie/blog/waterfall-methodology/ [Accessed 24 Nov. 2023].

Wang, J., Yang, Y., Wang, T., Sherratt, R.S. and Zhang, J., 2020. Big data service architecture: a survey. Journal of Internet Technology, 21(2), pp.393-405.